Problem Chosen
E
2023
MCM/ICM
Summary Sheet
Team Control Number
2307336
Protect the Night, Control the Light!
Summary
People generally are struck by the ’beauty’ of city lights without realizing that these
are also images of pollution, like admiring the beauty of the rainbow colours that gaso-
line produces in water and not recognising that it is chemical pollution. In this paper,
we construct a broadly applicable Light Pollution Risk Assessment Model to assess the
risk level of a given location, and propose an Intervention Strategy Model to mitigate
the effects of light pollution in various locations.
For Task 1, we propose a Light Pollution Risk Assessment Model. The model
integrates risks from four dimensions: humans, wildlife, plants, and energy waste
caused by light pollution. Considering multiple related indicators, EWM-TOPSIS is
applied to solve the overall risk score, which is divided into four levels: fragile(0-1),
poor(1-2), ordinary(2-3), and good(3-4).
For Task 2, the Light Pollution Risk Assessment Model is applied to four typical
regions in Shenzhen, representing urban, suburban, rural, and protected areas. In the
data preparation phase, we use nighttime remote sensing and multi-spectral remote
sensing data to estimate the Normalized Difference Vegetation Index (NDVI), night-
time radiance, and population density in the study areas. The protected area has a risk
score of 0.357992, while the rural community has a risk score of 1.859474, the subur-
ban community has a risk score of 2.114942, and the urban community has a risk score
of 3.19662. These scores correspond to the fragile, poor, ordinary, and good levels,
respectively.
For Task 3, we develop three intervention strategies including improving the light
source, lowering the lighting intensity and optimizing regional light layouts. Then
we list multiple specific actions for each strategy. Intervention Strategy Model based
on Differential Equation is established to quantify how three strategies impact the
risk level.
For Task 4, we select urban and suburban communities to verify the effectiveness
of the three intervention strategies. Over the next 50 years, the risk scores after the
implementation of the three strategies are reduced by approximately 2%, 6%, and 3%
respectively. It can be concluded that the second strategy, lowering the lighting in-
tensity that aims to reduce the total amount of light radiance is the most effective
intervention strategy for both urban and suburban areas.
Finally, the sensitivity analysis of the risk assessment model shows that the fluctua-
tion of a single evaluation indicator by -10% - 10% has a reasonable impact on the final
risk score shown in Figure 16. Therefore, the model is robust to changes in a single
indicator. Besides, the sensitivity analysis of the Strategy Model shown in Figure 17
means that our model is robust to the growth rate.
Keywords: Light Pollution; Intervention strategies; EWM-TOPSIS; Differential Equa-
tion
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